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Facial muscle activity and EEG recordings: redundancy analysis
Electroencephalography and clinical Neurophysiology, 79 (1991) 358 360 {~ 1991 Elsevier Scientific Publishers Ireland, Ltd. 0013-4649/91/$03.50 ADONIS 001346499100154U
358
E E G 9/)223
Facial muscle activity and EEG recordings: redundancy analysis Bruce H. Friedman and Julian F. Thayer Pennsylcania State Unicersity, Unicersity Park, PA 16802 (U.S.A.) (Accepted for publication: 1 July I991)
Summary
The present study explored the use of redundancy analysis, a multivariate technique for assessment of interset association, to examine facial muscle contamination of E E G recordings in studies involving covert levels of emotional expression. Redundancy analyses were performed on simultaneously recorded E E G and facial E M G data obtained in an emotion induction paradigm. Redundancy indices obtained suggest that (1) the amount of variance in E E G activity that can be explained by facial muscle activity under such conditions is minimal, and (2) the E E G alpha band may be at least as or even more susceptible to muscle contamination as the beta band.
Key words: EEG; EMG; Artifact; Redundancy analysis
A potentially important and largely unresolved issue for E E G researchers is contamination of the E E G signal by facial muscle activity. This may be of particular concern in studies involving induced emotion, which are likely to elicit facial expressions (Davidson et al. 1990). One approach which has been used to indicate the degree of E M G artifact in E E G is coherence analysis (O'Donnell et al. 1974). Coherence analysis, cross-correlation, and cross-spectral density analysis have been the most widely used methods of assessing inter-electrode relationships and shared variance (e.g., Bohdanecky et al. 1982; Gasser et al. 1987). However, a limitation of such measures is that they yield symmetric indices; there is an implied mutual dependence between activity at different sites. It makes no difference which site is designated as criterion and which as predictor - - the value of the index remains the same (Cramer and Nicewander 1979). Unless one assumes that E E G contamination by E M G is equal to the converse, symmetric measures may not adequately characterize the nature of such relationships. Redundancy analysis is useful in situations where variables in one set may be viewed as predictive of the amount of variance in the other set, as opposed to mutual dependence (Lambert et al. 1988). Redundancy indices yield the maximum proportion of variance that can be explained in the set of criterion variables by the predictor variables; they are directional and hence
Correspondence to: Dr. Bruce H. Friedman or Julian F. Thayer, D e p a r t m e n t of Psychology, Moore Building, University Park, PA 16802 (U.S.A.). Tel.: (814) 865-1671.
asymmetric. This may be particularly valuable in exploring interset relationships such as those between E M G and EEG. An additional benefit is that as a multivariate technique it can handle simultaneous examination of multiple sites and bandwidths. Alternatively, coherence analysis is directly analogous to the correlation coefficient of classical statistics (Tucker et al. 1986), and therefore is limited to concurrent assessment of two sites. There are a number of issues which have been raised in the muscle a r t i f a c t / E E G literature that could potentially be clarified with redundancy analysis. Several researchers have suggested that muscle artifact is most pronounced in the beta (13-20 Hz) band of the E E G (O'Donnell et al. 1974; Davidson 1988). However, potentially conflicting information can be found in these citations. Davidson (1988) cautions that lowpass filtering of the E E G at 40 Hz will not affect E M G activity in the E E G alpha (8-13 Hz) range, leaving open the possibility of contamination in that band as well as beta. O'Donnell et al. (1974) found high coherences between frontal E E G and frontalis E M G sites across both alpha (8-13 Hz) and beta (14-19 Hz) bands, as well as large absolute changes in E E G alpha and power during frontalis contraction. Others have found significant facial muscle activity at frequencies below beta (Nunez 1981; Van Boxtel et al. 1984) that may be a source of artifact in E E G recordings. Another concern is the relative effect of muscle contraction level. Davidson et al. (1990) suggest that in studies involving affective inductions, the potential for contamination of the E E G by facial muscle activity may be particularly salient, especially when visible facial expressions are being used as epoch markers of discrete emotion. However, many studies have shown
EMG AND I:.UG R E D U N D A N C Y
that covert levcls of facial muscle activity discernible only by E M G recording (as is typically induced by "milder' affective stimuli such as imagery or music) can be used to reliably discriminate between discrete emotion statcs (see Cacioppo et al. 1990, for review); presumably the potential for artifact would be less under these conditions. In O'Donnell et al. (1974), muscle contraclion was strongly held for 90 sec, making gcneralizations to studies which elicit low levels of facial muscle activity for brief periods problematic. A final caveat to the O'Donnell ct al. study is their use of non-standard clectrode placement, making comparisons to typical E E G studies of emotion difficult. To address these issues, a study was conducted involving the clicitation of emotion using music and imagery as aftective stimuli. Recordings were taken from multiple E E G and E M G sites across the alpha and beta frequcncy bands during the emotion inductions. Redundancy indices were calculated on this data to assess intersite relationships between E M G and E E G activity.
Method
Thc subjects wcrc 17 students (mean age 2(/.9 years) recruited from an undergraduate psychology course. EEG activity was recorded from left and right frontal (F3 and F4) and parietal (P3 and P4) sites, referenced to linked cars, a commonly used procedure in studies of this nature (e.g., "Fucker et al. 1986; Gasser et al. 19871. E M G activity was recorded from 4 facial sites: left and right zygomaticus (cheek) and corrugator (brow) areas, according to the placcment guidelines in Cacioppo ct al. (1990). These scalp and facial sites were selected because of their putative ability in discriminating between" positive and negative affect (sue Silberman and Weingartner 1986; Cacioppo et al. 1990; Davidson et al. 199(/, for reviews). All electrode impedances were less than 5 k,Q. The raw E M G signals werc amplified by Grass 7P3 pre-amplifiers, with a half-amplitude low frequency cutoff at 0.3 Hz, and by Grass 7DA driver amplifiers, with high frequency cutoff of 40 kHz. The E E G signals were amplified by Grass 7P5 l 1 E E G amplifiers, with a low frequenc~ cutoff at 1 Hz, and a high frequency cutoff at 30 kHz. The signals were further filtered digitally using a Butterworth type low-pass filter, down 6 dB at 225 Hz for E M G and at 30 Hz for E E G . All signals were sampled and digitized at 1024 Hz, digitally filtcred, and down sampled to 512 Hz for E M G and 64 Hz for E E G with an MBC D A S H 16 A / D converter and turbo-pascal software. Therefore, the vast majority of E M G power above the Nyquist frequency of 256 Hz was removed bcfore the sampling rate was decreased. The majority of E M G power has been found to lie
35q
between 1{} and 200 Hz (Caeioppo et al. 1990); the original sampling rate of 1(}24 Hz was selected to insure against any possible aliasing from signals above that. Fast Fourier Transforms were performed on thc digitized data. For EMG, spectral estimates were obtained using a Hamming window, sampling 2 sec epochs at 512 Hz with a 5(1% overlap between epochs. Total power was calculated over the 0-256 Hz range in each 20 scc baseline and emotion-induccd epoch, taking the average of the 2 sec epochs. KEG spectral estimates were also obtained by use of a Hamming window, sampling over 16 sue of data at 64 Hz, yielding power in the alpha (8.0-13.5 Hz) and beta (13.51-21.0 t4z) bands. It should be noted that these measures were used with the intention of obtaining stable narrow-band estimates for other questions not addressed hl the present paper. Each subject participated in 2 types of emotion induction conditions: listening to affcctively laden music and self-generating imagery. Each emotion induction type consisted of 4 designated emotions - - happiness, sadness, serenity, and agitation. Scores were expresscd in terms of changes from baselines, calculated by subtracting power in the 20 sec emotion induction epochs from power in thc 20 scc pre-condition baseline epochs. Each subject received all 8 emotion inductions, and individual data was then group aggregated across all conditions.
Results
Manipulation check Initial pattern classification analysis of the E E G and E M G data indicated that inductions elicited E E G and E M G activity that could reliably discriminate among the affective conditions. The classification accuracy among induction conditions when using the combined E E G and E M G data was 41.2%,, reliably above chance values according to r e c o m m e n d e d criteria (Hair et al. 1987). Therefore, it was concluded that the inductions elicited discrete patterns of E E G and E M G activity associated with each affective condition.
Redlmdano' analyses Redundancy analysis indicated that overall, the amount of variance in the E E G that could be explained by the E M G when all sites and frequencies were employed (i.e., when the redundancy index of left and right brow and cheek muscle activity was calculated on F3, F4, P3, and P4 alpha and beta power) was minimal (8.22%), as was the variance in E M G activity predicted by the E E G variables (3.08%). Of particular interest are the frontal E E G sites, where contamination is presumed to be largest: using power in both
361)
frequency bands, brow (corrugator) activity accounted for 6.91% of the variance in frontal E E G activity, while frontal E E G activity predicted 3.02% of the variance in brow E M G activity. Furthermore, separate redundancy analyses indicated that brow E M G could account for more variance in frontal alpha (4.38%) than beta (2.97%). Cheek (zygomatic) E M G accounted for a negligible amount of variance in total parietal activity (1.28%); again, this was still greater in alpha (1.21%) than beta (0.36%).
B.H. FRIEDMAN, J.F. THAYER
greater range of frequency bands, and varying levels of facial muscle activity. Such explorations, coupled with comparisons of results from more commonly used techniques such as coherence analysis, may help clarify the significant yet largely unsettled concern of facial muscle artifact in E E G recordings, particularly in studies involving emotional expression. This research was partially supported by a Research Initiation Grant awarded to the second author by The Pennsylvania State University. The authors would like to thank Michael W. Vasey for his programming assistance in data acquisition and quantification,
Discussion
First, the finding that muscle contamination is greater when the E E G electrode is near a contracting electrode (O'Donnell et al. 1974) was predictably replicated. The results also suggest that in studies involving covert levels of facial muscle activity, contamination of E E G recordings may not be a significant concern. This is evidenced by the very low proportion of variance in E E G activity that generally could be accounted for by E M G activity. Hence, it is likely that contamination is potentiated in studies involving strongly contracting facial muscles or visible facial expressions. Furthermore, greater amounts of EMG-predicted variance were found in alpha than in beta at both frontal and parietal E E G sites. These data indicate that one needs to consider the possibility of muscle artifact in alpha as well as beta, when there is a reasonable concern about E E G contamination. It has been found that the E M G power spectrum shifts to lower frequencies with increases in contraction strength (Van Boxtel et al. 1983), so it is possible that this effect would be even more pronounced in studies involving visible facial expression. Moreover, one should not assume that all of the variance explained by the redundancy index of E M G on E E G is due to muscle artifact. This shared variance may also reflect cortical-somatic interactions involved in affective processes, the nature of which (as well as degree of influence) are open to speculation. Therefore, the redundancy indices of E M G on E E G may in fact overestimate the effect of facial muscle activity on E E G recordings. These data demonstrate that redundancy analysis may be valuable in the assessment of facial muscle artifact in E E G recordings, and is an instructive supplement to techniques such as coherence analysis. The present study supports its use in future research, involving perhaps a larger montage of electrode sites, a
References Bohdanecky, Z., Lansky, P. and Radii, T. An integral measure of the coherence function between pairs of EEG recordings. Electroenceph. clin. Neurophysiol., 1982, 54: 587-590. Cacioppo, J.T., Tassinary, L.G. and Fridlund, A.J. The skeletomotor system. In: J.T. Cacioppo and L.G. Tassinary (Eds.), Principles of Psychophysiology: Physical, Social, and Inferential Elements. Cambridge University Press, Cambridge, 1990: 325-384. Cramer, E.M. and Nicewander, W.A. Some symmetric, invariant measures of multivariate association. Psychometrika, 1979, 44: 43 54. Davidson, R.J. EEG measures of cerebral asymmetry: conceptual and methodological issues. Int. J. Neurosci., 1988, 39: 71-89. Davidson, R.J., Ekman, P., Friesen, W.V., Saron, C.D. and Senulis, J.A. Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology I. J. Pers. Soc. Psychol., 1990, 58: 330-341. Gasser, T., Jennen-Steinmetz, C. and Verleger, R. EEG coherence at rest and during a visual task in two groups of children. Electroenceph. clin. Neurophysiol., 1987, 67: 151-158. Hair, J.F., Anderson, R.E. and Tatham, R.L. Multivariate Data Analysis. Macmillan, New York, 1987. Lambert, Z.V., Wildt, A.R. and Durand, R.M. Redundancy analysis: an alternative to canonical correlation and multivariate multiple regression in exploring interset associations. Psychol. Bull., 1988, 104: 282-289. Nunez, P. Electrical Fields of the Brain. Oxford University Press, New York, 1981. O'Donnell, R.D., Berkhout, J. and Adey, W.R. Contamination of scalp EEG spectrum during contraction of cranio-facial muscles. Electroenceph. clin. Neurophysiol., 1974, 37: 145-151. Silberman, E.K. and Weingartner, H. Hemispheric lateralization of functions related to emotion. Brain Cognit., 1986, 5: 322-353. Tucker, D.M., Roth, D.L. and Bair, T.B. Functional connections among cortical regions: topography of EEG coherence. Electroenceph, clin. Neurophysiol., 1986, 63: 242-250. Van Boxtel, A., Goudswaard, P., Van der Molen, G.M. and Van den Bosch, W.E.J. Changes in electromyogram power spectra of facial and jaw-elevator muscles during fatigue. J. Appl. Physiol., 1983, 54: 51-58. Van Boxtel, A., Goudswaard, P. and Schomaker, L.R.B. Amplitude and bandwidth of the frontalis surface EMG: effects of electrode parameters. Psychophysiology, 1984, 21: 699-707.
358
E E G 9/)223
Facial muscle activity and EEG recordings: redundancy analysis Bruce H. Friedman and Julian F. Thayer Pennsylcania State Unicersity, Unicersity Park, PA 16802 (U.S.A.) (Accepted for publication: 1 July I991)
Summary
The present study explored the use of redundancy analysis, a multivariate technique for assessment of interset association, to examine facial muscle contamination of E E G recordings in studies involving covert levels of emotional expression. Redundancy analyses were performed on simultaneously recorded E E G and facial E M G data obtained in an emotion induction paradigm. Redundancy indices obtained suggest that (1) the amount of variance in E E G activity that can be explained by facial muscle activity under such conditions is minimal, and (2) the E E G alpha band may be at least as or even more susceptible to muscle contamination as the beta band.
Key words: EEG; EMG; Artifact; Redundancy analysis
A potentially important and largely unresolved issue for E E G researchers is contamination of the E E G signal by facial muscle activity. This may be of particular concern in studies involving induced emotion, which are likely to elicit facial expressions (Davidson et al. 1990). One approach which has been used to indicate the degree of E M G artifact in E E G is coherence analysis (O'Donnell et al. 1974). Coherence analysis, cross-correlation, and cross-spectral density analysis have been the most widely used methods of assessing inter-electrode relationships and shared variance (e.g., Bohdanecky et al. 1982; Gasser et al. 1987). However, a limitation of such measures is that they yield symmetric indices; there is an implied mutual dependence between activity at different sites. It makes no difference which site is designated as criterion and which as predictor - - the value of the index remains the same (Cramer and Nicewander 1979). Unless one assumes that E E G contamination by E M G is equal to the converse, symmetric measures may not adequately characterize the nature of such relationships. Redundancy analysis is useful in situations where variables in one set may be viewed as predictive of the amount of variance in the other set, as opposed to mutual dependence (Lambert et al. 1988). Redundancy indices yield the maximum proportion of variance that can be explained in the set of criterion variables by the predictor variables; they are directional and hence
Correspondence to: Dr. Bruce H. Friedman or Julian F. Thayer, D e p a r t m e n t of Psychology, Moore Building, University Park, PA 16802 (U.S.A.). Tel.: (814) 865-1671.
asymmetric. This may be particularly valuable in exploring interset relationships such as those between E M G and EEG. An additional benefit is that as a multivariate technique it can handle simultaneous examination of multiple sites and bandwidths. Alternatively, coherence analysis is directly analogous to the correlation coefficient of classical statistics (Tucker et al. 1986), and therefore is limited to concurrent assessment of two sites. There are a number of issues which have been raised in the muscle a r t i f a c t / E E G literature that could potentially be clarified with redundancy analysis. Several researchers have suggested that muscle artifact is most pronounced in the beta (13-20 Hz) band of the E E G (O'Donnell et al. 1974; Davidson 1988). However, potentially conflicting information can be found in these citations. Davidson (1988) cautions that lowpass filtering of the E E G at 40 Hz will not affect E M G activity in the E E G alpha (8-13 Hz) range, leaving open the possibility of contamination in that band as well as beta. O'Donnell et al. (1974) found high coherences between frontal E E G and frontalis E M G sites across both alpha (8-13 Hz) and beta (14-19 Hz) bands, as well as large absolute changes in E E G alpha and power during frontalis contraction. Others have found significant facial muscle activity at frequencies below beta (Nunez 1981; Van Boxtel et al. 1984) that may be a source of artifact in E E G recordings. Another concern is the relative effect of muscle contraction level. Davidson et al. (1990) suggest that in studies involving affective inductions, the potential for contamination of the E E G by facial muscle activity may be particularly salient, especially when visible facial expressions are being used as epoch markers of discrete emotion. However, many studies have shown
EMG AND I:.UG R E D U N D A N C Y
that covert levcls of facial muscle activity discernible only by E M G recording (as is typically induced by "milder' affective stimuli such as imagery or music) can be used to reliably discriminate between discrete emotion statcs (see Cacioppo et al. 1990, for review); presumably the potential for artifact would be less under these conditions. In O'Donnell et al. (1974), muscle contraclion was strongly held for 90 sec, making gcneralizations to studies which elicit low levels of facial muscle activity for brief periods problematic. A final caveat to the O'Donnell ct al. study is their use of non-standard clectrode placement, making comparisons to typical E E G studies of emotion difficult. To address these issues, a study was conducted involving the clicitation of emotion using music and imagery as aftective stimuli. Recordings were taken from multiple E E G and E M G sites across the alpha and beta frequcncy bands during the emotion inductions. Redundancy indices were calculated on this data to assess intersite relationships between E M G and E E G activity.
Method
Thc subjects wcrc 17 students (mean age 2(/.9 years) recruited from an undergraduate psychology course. EEG activity was recorded from left and right frontal (F3 and F4) and parietal (P3 and P4) sites, referenced to linked cars, a commonly used procedure in studies of this nature (e.g., "Fucker et al. 1986; Gasser et al. 19871. E M G activity was recorded from 4 facial sites: left and right zygomaticus (cheek) and corrugator (brow) areas, according to the placcment guidelines in Cacioppo ct al. (1990). These scalp and facial sites were selected because of their putative ability in discriminating between" positive and negative affect (sue Silberman and Weingartner 1986; Cacioppo et al. 1990; Davidson et al. 199(/, for reviews). All electrode impedances were less than 5 k,Q. The raw E M G signals werc amplified by Grass 7P3 pre-amplifiers, with a half-amplitude low frequency cutoff at 0.3 Hz, and by Grass 7DA driver amplifiers, with high frequency cutoff of 40 kHz. The E E G signals were amplified by Grass 7P5 l 1 E E G amplifiers, with a low frequenc~ cutoff at 1 Hz, and a high frequency cutoff at 30 kHz. The signals were further filtered digitally using a Butterworth type low-pass filter, down 6 dB at 225 Hz for E M G and at 30 Hz for E E G . All signals were sampled and digitized at 1024 Hz, digitally filtcred, and down sampled to 512 Hz for E M G and 64 Hz for E E G with an MBC D A S H 16 A / D converter and turbo-pascal software. Therefore, the vast majority of E M G power above the Nyquist frequency of 256 Hz was removed bcfore the sampling rate was decreased. The majority of E M G power has been found to lie
35q
between 1{} and 200 Hz (Caeioppo et al. 1990); the original sampling rate of 1(}24 Hz was selected to insure against any possible aliasing from signals above that. Fast Fourier Transforms were performed on thc digitized data. For EMG, spectral estimates were obtained using a Hamming window, sampling 2 sec epochs at 512 Hz with a 5(1% overlap between epochs. Total power was calculated over the 0-256 Hz range in each 20 scc baseline and emotion-induccd epoch, taking the average of the 2 sec epochs. KEG spectral estimates were also obtained by use of a Hamming window, sampling over 16 sue of data at 64 Hz, yielding power in the alpha (8.0-13.5 Hz) and beta (13.51-21.0 t4z) bands. It should be noted that these measures were used with the intention of obtaining stable narrow-band estimates for other questions not addressed hl the present paper. Each subject participated in 2 types of emotion induction conditions: listening to affcctively laden music and self-generating imagery. Each emotion induction type consisted of 4 designated emotions - - happiness, sadness, serenity, and agitation. Scores were expresscd in terms of changes from baselines, calculated by subtracting power in the 20 sec emotion induction epochs from power in thc 20 scc pre-condition baseline epochs. Each subject received all 8 emotion inductions, and individual data was then group aggregated across all conditions.
Results
Manipulation check Initial pattern classification analysis of the E E G and E M G data indicated that inductions elicited E E G and E M G activity that could reliably discriminate among the affective conditions. The classification accuracy among induction conditions when using the combined E E G and E M G data was 41.2%,, reliably above chance values according to r e c o m m e n d e d criteria (Hair et al. 1987). Therefore, it was concluded that the inductions elicited discrete patterns of E E G and E M G activity associated with each affective condition.
Redlmdano' analyses Redundancy analysis indicated that overall, the amount of variance in the E E G that could be explained by the E M G when all sites and frequencies were employed (i.e., when the redundancy index of left and right brow and cheek muscle activity was calculated on F3, F4, P3, and P4 alpha and beta power) was minimal (8.22%), as was the variance in E M G activity predicted by the E E G variables (3.08%). Of particular interest are the frontal E E G sites, where contamination is presumed to be largest: using power in both
361)
frequency bands, brow (corrugator) activity accounted for 6.91% of the variance in frontal E E G activity, while frontal E E G activity predicted 3.02% of the variance in brow E M G activity. Furthermore, separate redundancy analyses indicated that brow E M G could account for more variance in frontal alpha (4.38%) than beta (2.97%). Cheek (zygomatic) E M G accounted for a negligible amount of variance in total parietal activity (1.28%); again, this was still greater in alpha (1.21%) than beta (0.36%).
B.H. FRIEDMAN, J.F. THAYER
greater range of frequency bands, and varying levels of facial muscle activity. Such explorations, coupled with comparisons of results from more commonly used techniques such as coherence analysis, may help clarify the significant yet largely unsettled concern of facial muscle artifact in E E G recordings, particularly in studies involving emotional expression. This research was partially supported by a Research Initiation Grant awarded to the second author by The Pennsylvania State University. The authors would like to thank Michael W. Vasey for his programming assistance in data acquisition and quantification,
Discussion
First, the finding that muscle contamination is greater when the E E G electrode is near a contracting electrode (O'Donnell et al. 1974) was predictably replicated. The results also suggest that in studies involving covert levels of facial muscle activity, contamination of E E G recordings may not be a significant concern. This is evidenced by the very low proportion of variance in E E G activity that generally could be accounted for by E M G activity. Hence, it is likely that contamination is potentiated in studies involving strongly contracting facial muscles or visible facial expressions. Furthermore, greater amounts of EMG-predicted variance were found in alpha than in beta at both frontal and parietal E E G sites. These data indicate that one needs to consider the possibility of muscle artifact in alpha as well as beta, when there is a reasonable concern about E E G contamination. It has been found that the E M G power spectrum shifts to lower frequencies with increases in contraction strength (Van Boxtel et al. 1983), so it is possible that this effect would be even more pronounced in studies involving visible facial expression. Moreover, one should not assume that all of the variance explained by the redundancy index of E M G on E E G is due to muscle artifact. This shared variance may also reflect cortical-somatic interactions involved in affective processes, the nature of which (as well as degree of influence) are open to speculation. Therefore, the redundancy indices of E M G on E E G may in fact overestimate the effect of facial muscle activity on E E G recordings. These data demonstrate that redundancy analysis may be valuable in the assessment of facial muscle artifact in E E G recordings, and is an instructive supplement to techniques such as coherence analysis. The present study supports its use in future research, involving perhaps a larger montage of electrode sites, a
References Bohdanecky, Z., Lansky, P. and Radii, T. An integral measure of the coherence function between pairs of EEG recordings. Electroenceph. clin. Neurophysiol., 1982, 54: 587-590. Cacioppo, J.T., Tassinary, L.G. and Fridlund, A.J. The skeletomotor system. In: J.T. Cacioppo and L.G. Tassinary (Eds.), Principles of Psychophysiology: Physical, Social, and Inferential Elements. Cambridge University Press, Cambridge, 1990: 325-384. Cramer, E.M. and Nicewander, W.A. Some symmetric, invariant measures of multivariate association. Psychometrika, 1979, 44: 43 54. Davidson, R.J. EEG measures of cerebral asymmetry: conceptual and methodological issues. Int. J. Neurosci., 1988, 39: 71-89. Davidson, R.J., Ekman, P., Friesen, W.V., Saron, C.D. and Senulis, J.A. Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology I. J. Pers. Soc. Psychol., 1990, 58: 330-341. Gasser, T., Jennen-Steinmetz, C. and Verleger, R. EEG coherence at rest and during a visual task in two groups of children. Electroenceph. clin. Neurophysiol., 1987, 67: 151-158. Hair, J.F., Anderson, R.E. and Tatham, R.L. Multivariate Data Analysis. Macmillan, New York, 1987. Lambert, Z.V., Wildt, A.R. and Durand, R.M. Redundancy analysis: an alternative to canonical correlation and multivariate multiple regression in exploring interset associations. Psychol. Bull., 1988, 104: 282-289. Nunez, P. Electrical Fields of the Brain. Oxford University Press, New York, 1981. O'Donnell, R.D., Berkhout, J. and Adey, W.R. Contamination of scalp EEG spectrum during contraction of cranio-facial muscles. Electroenceph. clin. Neurophysiol., 1974, 37: 145-151. Silberman, E.K. and Weingartner, H. Hemispheric lateralization of functions related to emotion. Brain Cognit., 1986, 5: 322-353. Tucker, D.M., Roth, D.L. and Bair, T.B. Functional connections among cortical regions: topography of EEG coherence. Electroenceph, clin. Neurophysiol., 1986, 63: 242-250. Van Boxtel, A., Goudswaard, P., Van der Molen, G.M. and Van den Bosch, W.E.J. Changes in electromyogram power spectra of facial and jaw-elevator muscles during fatigue. J. Appl. Physiol., 1983, 54: 51-58. Van Boxtel, A., Goudswaard, P. and Schomaker, L.R.B. Amplitude and bandwidth of the frontalis surface EMG: effects of electrode parameters. Psychophysiology, 1984, 21: 699-707.